Tuesday, 26 May 2009

Dynamic range compression is a technique used by audio engineers to optimise the distribution of frequencies in a mix. In popular music it is often abused, resulting in a flat and overly loud sound. An audio engineer applies some static parameters of threshold, knee, compression ratio, attack, delay and gain based on the typical listening environment of their anticipated user. This is why you can hear your favourite top 40 track in a noisy environment but you can barely hear classical music at the same volume level on your audio device. Therefore, I propose that these parameters should adapt to the environment of the user. If it is noisy, the compression ratio is pushed up and if it is quiet it can be relaxed. By intuition, I assume that this would be a relatively easy solution to solve with machine learning. My simple explanation with one of the parameters is trivial. I am sure more complex relationships exist between the parameters and user satisfaction. If someone could embed this in a popular music player with the correct audio source, it could be a winning combination. However, this requires an unmastered, or a minimally mastered audio source.